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Generating coordinated natural motion in human-like robotic structures has proved to be a challenging task. Given that humans easily solve this problem, we propose a methodology to devise the underlying strategies of human movement and apply them for robotic control. We use this approach to examine how humans utilize their muscles while performing(More)
A task-level control framework is proposed for providing feedback control in the simulation of goal-directed human motion. An operational space approach, adapted from the field of robotics, is used for this purpose. This approach is augmented by a significant new extension directed at addressing the control of muscle-driven systems. Task/posture(More)
The synthesis of human motion is a complex procedure that involves accurate reconstruction of movement sequences, modeling of musculoskeletal kinematics, dynamics and actuation, and characterization of reliable performance criteria. Many of these processes have much in common with the problems found in robotics research. Task-based methods used in robotics(More)
— This paper applies a task-level approach to the control of holonomically constrained shoulder models. These models include a biomechanical representation based on human physiology and a robotic design based on a parallel-serial structure. Both models involve complex kinematically coupled motion between the shoulder girdle and the humerus. This coupled(More)
—Improved resource utilization and fault tolerance of large-scale HPC systems can be achieved through fine-grained, intelligent, and dynamic resource (re)allocation. We explore components and enabling technologies applicable to creating a system to provide this capability: specifically 1) Scalable fine-grained monitoring and analysis to inform resource(More)
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—Accurate failure prediction in conjunction with efficient process migration facilities including some Cloud constructs can enable failure avoidance in large-scale high performance computing (HPC) platforms. In this work we demonstrate a prototype system that incorporates our prob-abilistic failure prediction system with virtualization mechanisms and(More)
—The neuromechanics of human motion are generally represented in the literature by feedforward control mechanisms: the brain sends a control signal to a part of the body to move, and motion ensues. Thus neuromechanical commands for motion are influenced by control signals from neurocognitive inputs. However, feedback also exists from the neuromechanical(More)